Product launching is a nuanced problem that entails choosing numerous decision variables, including timing, pricing, quality, and marketing strategy. Furthermore, decision-makers must account for multiple uncertainty sources. These include endogenous factors, such as product quality and the strategic decisions of competitors and consumers; as well as exogenous elements, like market evolution and regulations. This paper proposes a novel general framework to support product launch decisions in the presence of multiple competitors and buyers using adversarial risk analysis, mitigating the strong common-knowledge assumptions typical of standard game-theoretic approaches in this domain. We also introduce variants that account for multiple product purchases and integrate discrete choice models. To overcome the inherent computational intractability of modeling multi-agent strategic uncertainty, we deploy a simulation-optimization approach leveraging Bayesian optimization. Finally, we illustrate our proposed framework through a software launch case study focused on optimal timing, pricing, and quality decisions.

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